Short-term wind speed forecasting based on random forest model combining ensemble empirical mode decomposition and improved harmony search algorithm

References

• Abuella, M., and B. Chowdhury. 2019. Forecasting of solar power ramp events: Apost-processing approach. Renewable Energy 133:1380–92. doi:10.1016/j.renene.2018.09.005.
   Web of Science ®Google Scholar
• Ahmad, T., and H. Chen. 2019. Nonlinear autoregressive and random forest approaches to forecasting electricity load for utility energy management systems. Sustainable Cities and Society 45:460–73. doi:10.1016/j.scs.2018.12.013.
   Web of Science ®Google Scholar
• Ali, M., and R. Prasad. 2019. Significant wave height forecasting via an extreme learning machine model integrated with improved complete ensemble empirical mode decomposition. Renewable and Sustainable Energy Reviews 104:281–95. doi:10.1016/j.rser.2019.01.014.
   Web of Science ®Google Scholar
• Askarzadeh, A., and M. Zebarjadi. 2014. Wind power modeling using harmony search with a novel parameter setting approach. Journal of Wind Engineering and Industrial Aerodynamics 135:70–75. doi:10.1016/j.jweia.2014.10.012.
   Web of Science ®Google Scholar
• Bagiorgas, H. S., M. Giouli, S. Rehman, and L. M. Al-Hadhrami. 2011. Weibull parameters estimation using four different methods and most energy-carrying wind speed analysis. International Journal of Green Energy 8 (5):529–54. doi:10.1080/15435075.2011.588767.
   Web of Science ®Google Scholar
• Bao, L., T. Gneiting, E. P. Grimit, P. Guttorp, and A. E. Raftery. 2010. Bias correction and Bayesian model averaging for ensemble forecasts of surface wind direction. Monthly Weather Review 138:1811–21. doi:10.1175/2009MWR3138.1.
   Web of Science ®Google Scholar
• Bhatti, A. R., Z. Salam, B. Sultana, N. Rasheed, A. B. Awan, U. Sultana, and M. Younas. 2019. Optimized sizing of photovoltaic grid‐connected electric vehicle charging system using particle swarm optimization. International Journal of Energy Research 43:500–22. doi:10.1002/er.4287.
   Web of Science ®Google Scholar
• Breiman, L. 1996. Bagging predictors. Machine Learning 24:123–40. doi:10.1007/BF00058655.
   Web of Science ®Google Scholar
• Breiman, L. 2001. Random forests. Machine Learning 45:5–32. doi:10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• Brown, B. G., R. W. Katz, and A. H. Murphy. 1984. Time series models to simulate and forecast wind speed and wind power. Journal of Climate and Applied Meteorology 23 (8):1184–95. doi:10.1175/1520-0450(1984)023<1184:TSMTSA>2.0.CO;2.
   Google Scholar
• Cadenas, E., and W. Rivera. 2009. Short term wind speed forecasting in La Venta, Oaxaca, México, using artificial neural networks. Renewable Energy 34:274–78. doi:10.1016/j.renene.2008.03.014.
   Web of Science ®Google Scholar
• Cassola, F., and M. Burlando. 2012. Wind speed and wind energy forecast through Kalman filtering of numerical weather prediction model output. Applied Energy 99:154–66. doi:10.1016/j.apenergy.2012.03.054.
   Web of Science ®Google Scholar
• Davò, F., S. Alessandrini, S. Sperati, L. D. Monache, D. Airoldi, and M. T. Vespucci. 2016. Post-processing techniques and principal component analysis for regional wind power and solar irradiance forecasting. Solar Energy 134:327–38. doi:10.1016/j.solener.2016.04.049.
   Web of Science ®Google Scholar
• Dawson, C. W., R. J. Abrahart, and L. M. See. 2007. HydroTest:aweb-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts. Environmental Modelling & Software 22:1034–52. doi:10.1016/j.envsoft.2006.06.008.
   Web of Science ®Google Scholar
• Deris, A. M., A. M. Zain, and R. Sallehuddin. 2013. Hybrid GR-SVM for prediction of surface roughness in abrasive water jet machining. Meccanica 48:1937–45. doi:10.1007/s11012-016-0542-8.
   Web of Science ®Google Scholar
• Dhillon, J., A. Kumar, and S. K. Singal. 2016. A stochastic approach for the operation of a wind and pumped storage plant under a deregulated environment. International Journal of Green Energy 13 (1):55–62. doi:10.1080/15435075.2014.909358.
   Web of Science ®Google Scholar
• Du, J., H. Sun, Y. Cao, Y. Liu, L. Pan, and Y. Liu. 2019. Ensemble interpolation of missing wind turbine nacelle wind speed data in wind farms based on robust particle swarm optimized generalized regression neural network. International Journal of Green Energy 16 (14):1210–19. doi:10.1080/15435075.2019.1671396.
   Web of Science ®Google Scholar
• Ersoz, S., T. C. Akinci, H. S. Nogay, and G. Dogan. 2013. Determination of wind energy potential in Kirklareli-Turkey. International Journal of Green Energy 10 (1):103–16. doi:10.1080/15435075.2011.641702.
   Web of Science ®Google Scholar
• Geem, Z. W. 2008. Novel derivative of harmony search algorithm for discrete design variables. Applied Mathematics and Computation 199:223–30. doi:10.1016/j.amc.2007.09.049.
   Web of Science ®Google Scholar
• Geem, Z. W., J. H. Kim, and G. V. Loganathan. 2001. A new heuristic optimization algorithm: Harmonysearch. Simulation 76:60–68. doi:10.1177/003754970107600201.
   Google Scholar
• Hao, Y., and C. Tian. 2019. A novel two-stage forecasting model based on error factor and ensemble method for multi-step wind power forecasting. Applied Energy 238:368–83. doi:10.1016/j.apenergy.2019.01.063.
   Web of Science ®Google Scholar
• Huang, N. E., Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N. Yen, C. C. Tung, and H. H. Liu. 1998. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the royal society of London. Series A:mathematical. Physical and Engineering Sciences 454:903–95. doi:10.1098/rspa.1998.0193.
   Google Scholar
• Kaba, K., H. M. Kandirmaz, and M. Avci. 2017. Estimation of daily sunshine duration using support vector machines. International Journal of Green Energy 14 (4):430–41. doi:10.1080/15435075.2016.1265971.
   Web of Science ®Google Scholar
• Kamal, L., and Y. Z. Jafri. 1997. Time series models to simulate and forecast hourly averaged wind speed in Quetta, Pakistan. Solar Energy 61:23–32. doi:10.1016/S0038-092X(97)00037-6.
   Web of Science ®Google Scholar
• Kani, S. P., G. H. Riahy, and D. Mazhari. 2011. An innovative hybrid algorithm for very short-term wind speed prediction using linear prediction and markov chain approach. International Journal of Green Energy 8 (2):147–62. doi:10.1080/15435075.2010.548887.
   Web of Science ®Google Scholar
• Kavasseri, R. G., and K. Seetharaman. 2009. Day-ahead wind speed forecasting using f-ARIMA models. Renewable Energy 34:1388–93. doi:10.1016/j.renene.2008.09.006.
   Web of Science ®Google Scholar
• Kumar, D., and K. Chatterjee. 2017. Design and analysis of artificial bee-colony-based MPPT algorithm for DFIG-based wind energy conversion systems. International Journal of Green Energy 14 (4):416–29. doi:10.1080/15435075.2016.1261709.
   Web of Science ®Google Scholar
• Lahouar, A., and J. B. H. Slama. 2015. Day-ahead load forecast using random forest and expert input selection. Energy Conversion and Management 103:1040–51. doi:10.1016/j.enconman.2015.07.041.
   Web of Science ®Google Scholar
• Lahouar, A., and J. B. H. Slama. 2017. Hour-ahead wind power forecast based on random forests. Renewable Energy 109:529–41. doi:10.1016/j.renene.2017.03.064.
   Web of Science ®Google Scholar
• Landberg, L. 1999. Short-term prediction of the power production from wind farms. Journal of Wind Engineering and Industrial Aerodynamics 80:207–20. doi:10.1016/S0167-6105(98)00192-5.
   Web of Science ®Google Scholar
• Lazić, L., G. Pejanović, and M. Živković. 2010. Wind forecasts for wind power generation using the Eta model. Renewable Energy 35:1236–43. doi:10.1016/j.renene.2009.10.028.
   Web of Science ®Google Scholar
• Lerch, S., and T. L. Thorarinsdottir. 2013. Comparison of non-homogeneous regression models for probabilistic wind speed forecasting. Tellus A:Dynamic Meteorology and Oceanography 65 (1):21206. doi:10.3402/tellusa.v65i0.21206.
   Web of Science ®Google Scholar
• Li, C., Y. Tao, W. Ao, S. Yang, and Y. Bai. 2018. Improving forecasting accuracy of daily enterprise electricity consumption using a random forest based on ensemble empirical mode decomposition. Energy 165:1220–27. doi:10.1016/j.energy.2018.10.113.
   Web of Science ®Google Scholar
• Li, G., and J. Shi. 2010. On comparing three artificial neural networks for wind speed forecasting. Applied Energy 87:2313–20. doi:10.1016/j.apenergy.2009.12.013.
   Web of Science ®Google Scholar
• Li, Y., H. Shi, F. Han, Z. Duan, and H. Liu. 2019. Smart wind speed forecasting approach using various boosting algorithms, big multi-step forecasting strategy. Renewable Energy 135:540–53. doi:10.1016/j.renene.2018.12.035.
   Web of Science ®Google Scholar
• Lin, Y., U. Kruger, J. Zhang, Q. Wang, L. Lamont, and L. E. Chaar. 2015. Seasonal analysis and prediction of wind energy using random forests and ARX model structures. IEEE Transactions on Control Systems Technology 23:1994–2002. doi:10.1109/TCST.2015.2389031.
   Web of Science ®Google Scholar
• Liu, D., D. Niu, H. Wang, and L. Fan. 2014. Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm. Renewable Energy 62:592–97. doi:10.1016/j.renene.2013.08.011.
   Web of Science ®Google Scholar
• Liu, H., Z. Duan, F. Z. Han, and Y. F. Han. 2018. Big multi-step wind speed forecasting model based on secondary decomposition, ensemble method and error correction algorithm. Energy Conversion and Management 156:525–41. doi:10.1016/j.enconman.2017.11.049.
   Web of Science ®Google Scholar
• Liu, H., X. Mi, and Y. Li. 2018. Smart multi-step deep learning model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis, LSTM network and ELM. Energy Conversion and Management 159:54–64. doi:10.1016/j.enconman.2018.01.010.
   Web of Science ®Google Scholar
• Liu, H., J. Shi, and E. Erdem. 2013. An integrated wind power forecasting methodology: Interval estimation of wind speed, operation probability of wind turbine, and conditional expected wind power output of a wind farm. International Journal of Green Energy 10 (2):151–76. doi:10.1080/15435075.2011.647170.
   Web of Science ®Google Scholar
• Liu, Y., J. Shi, Y. Yang, and S. Han. 2009. Piecewise support vector machine model for short-term wind-power prediction. International Journal of Green Energy 6 (5):479–89. doi:10.1080/15435070903228050.
   Web of Science ®Google Scholar
• Ludwig, N., S. Feuerriegel, and D. Neumann. 2015. Putting big data analytics to work: Featureselection for forecasting electricity prices using the LASSO and random forests. Journal of Decision Systems 24:19–36. doi:10.1080/12460125.2015.994290.
   Web of Science ®Google Scholar
• Luo, X., J. Sun, L. Wang, W. Wang, W. Zhao, J. Wu, J. Wu, and Z. Zhang. 2018. Short-term wind speed forecasting via stacked extreme learning machine with generalized correntropy. IEEE Transactions on Industrial Informatics 14:4963–71. doi:10.1109/TII.2018.2854549.
   Web of Science ®Google Scholar
• Maatallah, O. A., A. Achuthan, K. Janoyan, and P. Marzocca. 2015. Recursive wind speed forecasting based on Hammerstein Auto-Regressive model. Applied Energy 145:191–97. doi:10.1016/j.apenergy.2015.02.032.
   Web of Science ®Google Scholar
• Mahdavi, M., M. Fesanghary, and E. Damangir. 2007. An improved harmony search algorithm for solving optimization problems. Applied Mathematics and Computation 188:1567–79. doi:10.1016/j.amc.2006.11.033.
   Web of Science ®Google Scholar
• Philippopoulos, K., D. Deligiorgi, and G. Karvounis. 2012. Wind speed distribution modeling in the greater area of Chania, Greece. International Journal of Green Energy 9 (2):174–93. doi:10.1080/15435075.2011.622020.
   Web of Science ®Google Scholar
• Prasad, R., R. C. Deo, Y. Li, and T. Maraseni. 2019. Weekly soil moisture forecasting with multivariate sequential, ensemble empirical mode decomposition and boruta-random forest hybridizer algorithm approach. Catena 177:149–66. doi:10.1016/j.catena.2019.02.012.
   Web of Science ®Google Scholar
• Qin, S., J. Wang, J. Wu, and G. Zhao. 2016. A hybrid model based on smooth transition periodic autoregressive and elman artificial neural network for wind speed forecasting of the Hebei region in China. International Journal of Green Energy 13 (6):595–607. doi:10.1080/15435075.2016.1212200.
   Web of Science ®Google Scholar
• Roulston, M. S., D. T. Kaplan, J. Hardenberg, and L. A. Smith. 2003. Using medium-range weather forcasts to improve the value of wind energy production. Renewable Energy 28:585–602. doi:10.1016/S0960-1481(02)00054-X.
   Web of Science ®Google Scholar
• Salcedo-Sanz, S., A. Pastor-Sánchez, J. Del Ser, L. Prieto, and Z. W. Geem. 2015. A coral reefs optimization algorithm with harmony search operators for accurate wind speed prediction. Renewable Energy 75:93–101. doi:10.1016/j.renene.2014.09.027.
   Web of Science ®Google Scholar
• Salkuti, S. R. 2019. Optimal power flow using multi-objective glowworm swarm optimization algorithm in a wind energy integrated power system. International Journal of Green Energy 16 (15):1547–61. doi:10.1080/15435075.2019.1677234.
   Web of Science ®Google Scholar
• Saravanakumar, R., and D. Jena. 2016. Nonlinear control of a wind turbine based on nonlinear estimation techniques for maximum power extraction. International Journal of Green Energy 13 (3):309–19. doi:10.1080/15435075.2014.952424.
   Web of Science ®Google Scholar
• Sharma, S. K., and S. Ghosh. 2016. Short-term wind speed forecasting: Application of linear and non-linear time series models. International Journal of Green Energy 13 (14):1490–500. doi:10.1080/15435075.2016.1212200.
   Web of Science ®Google Scholar
• Shi, J., X. Qu, and S. Zeng. 2011. Short-term wind power generation forecasting: Direct versus indirect ARIMA-based approaches. International Journal of Green Energy 8 (1):100–12. doi:10.1080/15435075.2011.546755.
   Web of Science ®Google Scholar
• Sloughter, J. M., T. Gneiting, and A. E. Raftery. 2010. Probabilistic wind speed forecasting using ensembles and Bayesian model averaging. Journal of the American Statistical Association 105:25–35. doi:10.1198/jasa.2009.ap08615.
   Web of Science ®Google Scholar
• Song, C., D. Liu, J. Wu, H. Wang, Y. Zhao, X. Zhao, and F. Dong. 2008. Multi-objective differential planning based on improved harmony search algorithm for power network. Electric Power Automation Equipment 34:142–48. doi:10.3969/j.issn.1006-6047.2014.11.022.
   Google Scholar
• Spyers-Ashby, J. M., P. G. Bain, and S. J. Roberts. 1998. A comparison of fast fourier transform (FFT) and autoregressive (AR) spectral estimation techniques for the analysis of tremor data. Journal of Neuroscience Methods 83:35–43. doi:10.1016/S0165-0270(98)00064-8.
   PubMed Web of Science ®Google Scholar
• Tascikaraoglu, A., and M. Uzunoglu. 2014. A review of combined approaches for prediction of short-term wind speed and power. Renewable and Sustainable Energy Reviews 34:243–54. doi:10.1016/j.rser.2014.03.033.
   Web of Science ®Google Scholar
• Ul Haque, A., and J. Meng. 2011. Short-term wind speed forecasting based on fuzzy ARTMAP. International Journal of Green Energy 8 (1):65–80. doi:10.1080/15435075.2010.529784.
   Web of Science ®Google Scholar
• Wang, D., H. Luo, O. Grunder, and Y. Lin. 2017. Multi-step ahead wind speed forecasting using an improved wavelet neural network combining variational mode decomposition and phase space reconstruction. Renewable Energy 113:1345–58. doi:10.1016/j.renene.2017.06.095.
   Web of Science ®Google Scholar
• Wang, J., L. Li, D. Niu, and Z. Niu. 2012a. An annual load forecasting model based on support vector regression with differential evolution algorithm. Applied Energy 94:65–70. doi:10.1016/j.apenergy.2012.01.010.
   Web of Science ®Google Scholar
• Wang, J. Z., and Y. Wang. 2017. A novel wind speed forecasting model for wind farms of Northwest China. International Journal of Green Energy 14 (5):463–78. doi:10.1080/15435075.2016.1278373.
   Web of Science ®Google Scholar
• Wang, S., N. Zhang, L. Wu, and Y. Wu. 2016. Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method. Renewable Energy 94:629–36. doi:10.1016/j.renene.2016.03.103.
   Web of Science ®Google Scholar
• Wang, T., M. Zhang, Q. Yu, and H. Zhang. 2012b. Comparing the applications of EMD and EEMD on time–frequency analysis of seismic signal. Journal of Applied Geophysics 83:29–34. doi:10.1016/j.jappgeo.2012.05.002.
   Web of Science ®Google Scholar
• Wei, N., C. Li, X. Peng, Y. Li, and F. Zeng. 2019. Daily natural gas consumption forecasting via the application of a novel hybrid model. Applied Energy 250:358–68. doi:10.1016/j.apenergy.2019.05.023.
   Web of Science ®Google Scholar
• Willmott, C. J. 1984. On the evaluation of model performance in physical geography. Spatial Statistics and Models 40:443–60. doi:10.1007/978-94-017-3048-8_23.
   Google Scholar
• Wu, J., J. Wang, S. Qin, and H. Lu. 2016. Suitable error evaluation criteria selection in the wind energy assessment via the K-means clustering algorithm. International Journal of Green Energy 13 (11):1145–62. doi:10.1080/15435075.2016.1175351.
   Web of Science ®Google Scholar
• Wu, Z., and N. E. Huang. 2009. Ensemble empirical mode decomposition: Anoise-assisted data analysis method. Advances in Adaptive Data Analysis 1:1–41. doi:10.1142/S1793536909000047.
   Google Scholar
• Yu, C., Y. Li, and M. Zhang. 2017. Comparative study on three new hybrid models using elman neural network and empirical mode decomposition based technologies improved by singular spectrum analysis for hour-ahead wind speed forecasting. Energy Conversion and Management 147:75–85. doi:10.1016/j.enconman.2017.05.008.
   Web of Science ®Google Scholar
• Yu, M., J. Zhu, and L. Yang. 2019. Short-term load prediction model combining FEW and IHS algorithm. Archives of Electrical Engineering 68:907–23. doi:10.24425/aee.2019.130691.
   Web of Science ®Google Scholar
• Zeng, B., H. Duan, Y. Bai, and W. Meng. 2018. Forecasting the output of shale gas in China using an unbiased grey model and weakening buffer operator. Energy 151:238–49. doi:10.1016/j.energy.2018.03.045.
   Web of Science ®Google Scholar
• Zhang, C., H. Wei, X. Zhao, T. Liu, and K. Zhang. 2016. A Gaussian process regression based hybrid approach for short-term wind speed prediction. Energy Conversion and Management 126:1084–92. doi:10.1016/j.enconman.2016.08.086.
   Web of Science ®Google Scholar
• Zhang, D., X. Peng, K. Pan, and Y. Liu. 2019. A novel wind speed forecasting based on hybrid decomposition and online sequential outlier robust extreme learning machine. Energy Conversion and Management 180:338–57. doi:10.1016/j.enconman.2018.10.089.
   Web of Science ®Google Scholar
• Zhang, Y., C. Zhang, Y. Zhao, and S. Gao. 2018. Wind speed prediction with RBF neural network based on PCA and ICA. Journal of Electrical Engineering 69:148–55. doi:10.2478/jee-2018-0018.
   Google Scholar
• Zhou, J., J. Shi, and G. Li. 2011. Fine tuning support vector machines for short-term wind speed forecasting. Energy Conversion and Management 52:1990–98. doi:10.1016/j.enconman.2010.11.007.
   Web of Science ®Google Scholar
• Zuluaga, C. D., M. A. Alvarez, and E. Giraldo. 2015. Short-term wind speed prediction based on robust Kalman filtering: Anexperimental comparison. Applied Energy 156:321–30. doi:10.1016/j.apenergy.2015.07.043.
   Web of Science ®Google Scholar